Scene understanding has become a fundamental research area in computer vision. To achieve this task, localizing and recognizing different objects in space is paramount. From the seminal work of face detection by Viola-Jones using cascaded Haar features to the state-of-the-art deep neural networks, object detection has evolved from just being used in limited cases to be being used extensively for detecting common and custom objects. Algorithm and hardware improvements currently allow for real time object detection on a smartphone. Typically, for each detected object, the object top-left co-ordinate along with bottom-right coordinate or width and height are returned. While this works for objects whose boundaries are orthogonal to the image boundaries, it struggles to accurately localize rotated or non-rectangular objects. By regressing for eight corner points instead of the traditional of the top-left and bottom-right of a rectangular box, we can mitigate these problems. Building up from anchor-free one-stage object detection methods, it is shown that object detection can also be used for arbitrary shaped bounding boxes.
Library of Congress Subject Headings
Pattern recognition systems; Computer vision; Image processing--Digital techniques; Machine learning
Computer Engineering (MS)
Department, Program, or Center
Computer Engineering (KGCOE)
Dey, Abhisek, "Convex Object Detection" (2020). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus